View Item 

Modelling volatility with mixture density networks

Access Status
Open access
Authors
Mostafa, Fahed
Dillon, Tharam S.
Date
2008
Collection
Type
Conference Paper
Metadata
Show full item record
Abstract

Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on the accuracy and completeness of each of these models.

Citation
Mostafa, Fahed and Dillon, Tharam. 2008. Modelling volatility with mixture density networks, in Hu, X.T. and Liu, Q. (ed), IEEE International Conference on Granular Computing, Aug 26 2008, pp. 501-505. Hangzhou, China: Institute of Electrical and Electronics Engineers (IEEE).
Source Title
Proceedings of the IEEE international conference on granular computing (GrC 2008)
URI
DOI
Faculty
Curtin Business School
School of Economics and Finance
School
Centre for Extended Enterprises and Business Intelligence
Remarks

Copyright © 2008 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.